Building Agent Memory with PostgreSQL
Exploring how PostgreSQL can become a durable memory layer for AI agents.
Agent memory sounds simple until you ask a harder question:
What should an agent actually remember?
A useful memory system is not just a database of previous conversations. It needs to decide what is worth keeping, how memories should be retrieved, and how old information should influence future reasoning.
Why PostgreSQL?
PostgreSQL is attractive because it can combine several kinds of memory in one place:
- structured state
- conversation history
- metadata
- relationships
- vector embeddings through pgvector
That makes it possible to treat memory as an engineering system rather than a single embedding lookup.
The problem with remembering everything
Saving everything creates a different problem: retrieval noise.
The memory system eventually needs policies around:
- what gets stored
- when it gets summarized
- how memories decay
- how relevance is calculated
- which memories are allowed back into context
This is where agent memory becomes much more interesting than simple chat history.
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